Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms Article Swipe
Qin Ding
,
Yiwei Liu
,
Cho‐Jui Hsieh
,
James Sharpnack
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.02979
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.02979
The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual bandit algorithms often have one or more hyper-parameters. As an example, in most optimal stochastic contextual bandit algorithms, there is an unknown exploration parameter which controls the trade-off between exploration and exploitation. A proper choice of the hyper-parameters is essential for contextual bandit algorithms to perform well. However, it is infeasible to use offline tuning methods to select hyper-parameters in contextual bandit environment since there is no pre-collected dataset and the decisions have to be made in real time. To tackle this problem, we first propose a two-layer bandit structure for auto tuning the exploration parameter and further generalize it to the Syndicated Bandits framework which can learn multiple hyper-parameters dynamically in contextual bandit environment. We derive the regret bounds of our proposed Syndicated Bandits framework and show it can avoid its regret dependent exponentially in the number of hyper-parameters to be tuned. Moreover, it achieves optimal regret bounds under certain scenarios. Syndicated Bandits framework is general enough to handle the tuning tasks in many popular contextual bandit algorithms, such as LinUCB, LinTS, UCB-GLM, etc. Experiments on both synthetic and real datasets validate the effectiveness of our proposed framework.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2106.02979
- https://arxiv.org/pdf/2106.02979
- OA Status
- green
- Cited By
- 1
- References
- 24
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W3170816163Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2106.02979Digital Object Identifier
- Title
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Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit AlgorithmsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-05Full publication date if available
- Authors
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Qin Ding, Yiwei Liu, Cho‐Jui Hsieh, James SharpnackList of authors in order
- Landing page
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https://arxiv.org/abs/2106.02979Publisher landing page
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https://arxiv.org/pdf/2106.02979Direct link to full text PDF
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2106.02979Direct OA link when available
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Regret, Computer science, Multi-armed bandit, Mathematical optimization, Artificial intelligence, Machine learning, Recommender system, Algorithm, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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| abstract_inverted_index.LinUCB, | 203 |
| abstract_inverted_index.between | 9, 60 |
| abstract_inverted_index.certain | 182 |
| abstract_inverted_index.dataset | 100 |
| abstract_inverted_index.further | 129 |
| abstract_inverted_index.general | 188 |
| abstract_inverted_index.machine | 28 |
| abstract_inverted_index.methods | 87 |
| abstract_inverted_index.offline | 85 |
| abstract_inverted_index.optimal | 45, 178 |
| abstract_inverted_index.perform | 77 |
| abstract_inverted_index.popular | 197 |
| abstract_inverted_index.propose | 117 |
| abstract_inverted_index.trials. | 24 |
| abstract_inverted_index.unknown | 53 |
| abstract_inverted_index.However, | 79 |
| abstract_inverted_index.UCB-GLM, | 205 |
| abstract_inverted_index.achieves | 177 |
| abstract_inverted_index.clinical | 23 |
| abstract_inverted_index.controls | 57 |
| abstract_inverted_index.datasets | 213 |
| abstract_inverted_index.example, | 42 |
| abstract_inverted_index.learning | 29 |
| abstract_inverted_index.multiple | 140 |
| abstract_inverted_index.problem, | 4, 114 |
| abstract_inverted_index.proposed | 154, 219 |
| abstract_inverted_index.systems, | 19 |
| abstract_inverted_index.validate | 214 |
| abstract_inverted_index.Moreover, | 175 |
| abstract_inverted_index.decisions | 103 |
| abstract_inverted_index.dependent | 165 |
| abstract_inverted_index.essential | 71 |
| abstract_inverted_index.framework | 136, 157, 186 |
| abstract_inverted_index.including | 17 |
| abstract_inverted_index.parameter | 55, 127 |
| abstract_inverted_index.structure | 121 |
| abstract_inverted_index.synthetic | 210 |
| abstract_inverted_index.trade-off | 8, 59 |
| abstract_inverted_index.two-layer | 119 |
| abstract_inverted_index.Syndicated | 134, 155, 184 |
| abstract_inverted_index.algorithms | 33, 75 |
| abstract_inverted_index.contextual | 2, 31, 47, 73, 92, 144, 198 |
| abstract_inverted_index.framework. | 220 |
| abstract_inverted_index.generalize | 130 |
| abstract_inverted_index.infeasible | 82 |
| abstract_inverted_index.scenarios. | 183 |
| abstract_inverted_index.stochastic | 1, 46 |
| abstract_inverted_index.Experiments | 207 |
| abstract_inverted_index.advertising | 21 |
| abstract_inverted_index.algorithms, | 30, 49, 200 |
| abstract_inverted_index.dynamically | 142 |
| abstract_inverted_index.environment | 94 |
| abstract_inverted_index.exploration | 10, 54, 61, 126 |
| abstract_inverted_index.recommender | 18 |
| abstract_inverted_index.environment. | 146 |
| abstract_inverted_index.applications, | 16 |
| abstract_inverted_index.effectiveness | 216 |
| abstract_inverted_index.exploitation, | 12 |
| abstract_inverted_index.exploitation. | 63 |
| abstract_inverted_index.exponentially | 166 |
| abstract_inverted_index.pre-collected | 99 |
| abstract_inverted_index.hyper-parameters | 69, 90, 141, 171 |
| abstract_inverted_index.hyper-parameters. | 39 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |